Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data

by: Yonghong Chen, Steven L Bressler, Mingzhou Ding

Journal of Neuroscience Methods, Vol. 150, No. 2. (30 January 2006), pp. 228-237.

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Referat

It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

@article{citeulike:482991, abstract = {It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.}, author = {Chen, Yonghong and Bressler, Steven L. and Ding, Mingzhou }, citeulike-article-id = {482991}, doi = {10.1016/j.jneumeth.2005.06.011}, journal = {Journal of Neuroscience Methods}, keywords = {methods, statistics}, month = {January}, number = {2}, pages = {228--237}, posted-at = {2008-06-05 11:18:20}, priority = {2}, title = {Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data}, url = {http://dx.doi.org/10.1016/j.jneumeth.2005.06.011}, volume = {150}, year = {2006} }

RIS record

TY - JOUR ID - citeulike:482991 L3 - citeulike-article-id:482991 TI - Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data JF - Journal of Neuroscience Methods VL - 150 IS - 2 SP - 228 EP - 237 N2 - It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method. KW - methods KW - statistics AU - Chen, Yonghong AU - Bressler, Steven AU - Ding, Mingzhou PY - 2006/01/30/ UR - http://dx.doi.org/10.1016/j.jneumeth.2005.06.011 ER -

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